To address the challenge of missing value imputation in time series data, especially the complexities of non-stationary time series, this paper proposes a novel framework DMA-MD (DiagMasked Attention for Non-stationary Time Series Imputation). This method aims to enhance imputation accuracy and model robustness, adapting to various data distribution changes in real-world applications. Firstly, DMA-MD employs a diagonal masking attention mechanism to improve imputation capability. This ensures that the model does not directly use target time point data for prediction but relies on information from other time steps, enhancing robustness and accuracy. Additionally, a stabilization module increases the model's adaptability to different data distributions, effectively mitigating the impact of non-stationarity and accelerating the training process. Extensive experiments on multiple real-world datasets, including Air Quality and ETT, demonstrate that DMA-MD significantly outperforms state-of-the-art methods such as Transformer and RNN-based approaches in imputation accuracy. Furthermore, ablation studies confirm the effectiveness of each component, showcasing DMA-MD's superior performance in handling complex non-stationary time series with missing data.